The advent of the Internet and Information Technology (“IT”) has revolutionized the way business is conducted around the world. Business organizations have adopted these technologies to organize their structure, work flow and business relationships and to make their business processes increasingly more efficient. In particular, these technologies have become instrumental in enabling business organizations to manage the large amount of data transacted by them on a daily basis.
Efforts to optimize a wide variety of business processes have led business organizations to demand an increased granularity and complexity in the data that is input into their information systems. The data generated by a given business organization is typically stored in various databases across the business organization's information systems. A database is a collection of data that is organized so that its contents may be easily accessed, managed, and updated. The data stored in a database and the algorithms used to manipulate the data are represented in computer memory by a data structure, such as a queue, linked list, stack, heap, tree, or hash table, among others.
The most prevalent type of database is the relational database, organized as a set of formally-described tables from which data may be accessed or reassembled in many different ways without having to reorganize the tables. Each table, also referred to as a relation, contains one or more data categories in columns. Each row in the table contains a unique instance of data for the categories defined by the columns. For example, an electronic commerce web site such as Amazon.com, of Seattle, Calif., may have a relational database to describe customers' orders. The relational database may include a table to store customers' personal information with columns for the customers' name, address, and credit card, and another table to describe the order itself, with columns for the product bought, its price and quantity. The rows in the tables would contain the instance of information for a particular customer and customer's order.
Data may be queried from a database using a standard application program interface called Structured Query Language (“SQL”). SQL enables an user to select, insert, delete, update, and find out the location of data, among other data operations. The user may specify SQL statements to manipulate data in a database as part of a relational database management system (“RDBMS”), which is a technology platform, set of services, or application for creating, updating, and administering a relational database. Examples of commercially available RDBMSs include DB2, sold by IBM Corporation, of White Plains, N.Y., Oracle 9i Database, sold by Oracle Corporation, of Redwood Shores, Calif., and OpenIngres, sold by Computer Associates International, Inc., of Islandia, N.Y. Alternatively, business organizations may use spreadsheet software applications such as Excel, sold by Microsoft Corporation, of Redmond, Wash., to emulate a RDBMS.
Business organizations often use commercially available RDBMSs to manage databases storing their customer, supplier, and internal data such as accounting and financial information, employees' records, inventory, and legal records, among others. Additionally, business organizations in industries such as construction and manufacturing may require more specialized RDBMSs to manage activity-based costing data involving various business activities and the determination of costs and cost drivers for each activity. An example of activity-based costing data includes the material and labor costs of a construction unit or the costs generated by a given purchase order or machine use. Commercially-available activity-based costing RDBMSs include Prolog Manager and Prolog Scheduler, sold by Meridian Project Systems, Inc., of Folsom, Calif., and the OneWorld software package, sold by J. D. Edwards & Company, of Denver, Colo.
A primary function of these activity-based costing systems is to highlight variances between actual costs and budgeted costs on an ongoing basis. To ensure accurate and consistent calculation of actual costs, these systems implement database queries that require the consideration of a large number of variables. These variables can include resource identification and classification, project, phase and task references, cost code references, and date and time, among others. Transactions that drive the calculation of actual costs in these systems must accurately record each of these variables in order to generate the correct results.
Using RDBMSs for such diverse purposes requires business organizations to invest a considerable amount of resources to train IT staff to manage the databases. Few of the RDBMSs available today allow an unsophisticated IT staff member to enter, view, and maintain multi-dimensional data efficiently and with a minimum of training. Multi-dimensional data consist of multiple data records that share a common field value. For example, in a customer database for an electronic commerce web site such as Amazon.com, of Seattle, Wash., the field “customer phone number” may be associated with multiple phone number records such as a home phone number, a work phone number, and a cellular phone number. While multi-dimensional data can be stored in a relational database, such data is more suitably stored in a multi-dimensional database that organizes the data according to their dimension. In the customer database example, a relational database would represent the different phone numbers by repeating the customer phone number field for each phone number record so that each phone number record would be in a different row in the table. In contrast, a multi-dimensional database would have a single row for the customer phone number field and multiple cells corresponding to the different phone number types, i.e., home, work, and cellular extending along a dimension that is logically orthogonal to the row/column dimensions of a standard table. Each multi-dimensional database is implemented to handle large amounts of data that are organized and queried in numerous ways, requiring the IT staff to create complex SQL programs to manipulate the data.
Additionally, the user interfaces provided with such RDBMSs are predominantly text based, have limited display areas, and require considerable training before an IT staff member can become proficient in their use. For example, activity-based costing RDBMSs become increasingly inefficient when the raw data contains many variables, or the resources are frequently applied to more than one activity within a recording period. The most efficient activity-based costing RDBMSs capture raw data in tables where each row represents one or more transactions and each column records or summarizes a different variable. Every time a variable changes value, either a new row is added to the table or another user interface screen is opened to show a details table wherein a new variable detail row is added.
To conserve display space, variable values are often represented using a shorthand code that is often numeric and non-descriptive, which leads to a cryptic display that is meaningless to IT staff members not familiar with the coding conventions. And despite this abbreviated representation technique, most solutions still require horizontal scrolling in the user interface to display all of the variables required by the transaction. Finally, given the repetitiveness of variables between rows, considerable time is spent entering repeated values and determining which variables are unique to a given row.
As a result of these difficulties with existing RDBMSs, several business organizations have elected to utilize expensive and specialized intermediaries to collect and transpose data into their information systems. The intermediaries function as applications service providers (“ASPs”) to enable the business organization to outsource its IT needs with the goal of minimizing the complications its own IT staff has to deal with when managing a RDBMS. Such an arrangement may be attractive for smaller organizations with limited IT resources or for larger organizations not interested in maintaining a large in-house IT staff. In both cases, however, the business organizations are still required to train employees to use the often cryptic and cumbersome user interfaces designed by the ASPs to manage the organizations' data. The same training and user interface difficulties found in commercially available RDBMSs are encountered with the solutions provided by ASPs. Furthermore, it may be more expensive for a business organization to outsource its IT database needs to an ASP than to purchase an off-the-shelf RDBMS due to the high consulting fees charged by the ASP to train a business organization's employee to use the ASP system.
Efficiently solving these database management difficulties requires building a RDBMS with a user interface that optimally uses a limited amount of display area to present information such that an IT staff member or other user is not required to navigate within or between large numbers of data entry screens to enter or view information. The interface should intuitively display information such that an unsophisticated user can be trained to use the RDBMS in a relatively short period of time. Additionally, to be as efficient as possible, the RDBMS must also recognize and capitalize upon opportunities to take advantage of as many data entry patterns as can be effectively automated.
In view of the foregoing, it would be desirable to provide systems and methods for representing, editing, and operating on multi-dimensional data efficiently without requiring extensive user training.
It further would be desirable to provide systems and methods for representing, editing, and operating on multi-dimensional data in a limited display area with graphical icons that identify different data units.
It also would be desirable to provide systems and methods for representing, editing, and operating on multi-dimensional data in a compact form by constraining data entered in a table to be within a given data context.
It also would be desirable to provide systems and methods for representing, editing, and operating on multi-dimensional data in a compact form so that there is no display repetition of multi-dimensional data in a given table.